Fine-tuned RoBERTa-large for detecting news on sport

Model Description

This model is a finetuned RoBERTa-large, for classifying whether news articles are about sport.

How to Use

from transformers import pipeline
classifier = pipeline("text-classification", model="dell-research-harvard/topic-sport")
classifier("Chelsea win Champions League")

Training data

The model was trained on a hand-labelled sample of data from the NEWSWIRE dataset.

Split	Size
Train	339
Dev	72
Test	72

Test set results

Metric	Result
F1	0.9412
Accuracy	0.9444
Precision	0.9697
Recall	0.9143

Citation Information

You can cite this dataset using

@misc{silcock2024newswirelargescalestructureddatabase,
      title={Newswire: A Large-Scale Structured Database of a Century of Historical News}, 
      author={Emily Silcock and Abhishek Arora and Luca D'Amico-Wong and Melissa Dell},
      year={2024},
      eprint={2406.09490},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2406.09490}, 
}

Applications

We applied this model to a century of historical news articles. You can see all the classifications in the NEWSWIRE dataset.